A comprehensive survey on deep learning based malware detection techniques

M Gopinath, SC Sethuraman - Computer Science Review, 2023 - Elsevier
Recent theoretical and practical studies have revealed that malware is one of the most
harmful threats to the digital world. Malware mitigation techniques have evolved over the …

A review on feature selection in mobile malware detection

A Feizollah, NB Anuar, R Salleh, AWA Wahab - Digital investigation, 2015 - Elsevier
The widespread use of mobile devices in comparison to personal computers has led to a
new era of information exchange. The purchase trends of personal computers have started …

Android malware familial classification and representative sample selection via frequent subgraph analysis

M Fan, J Liu, X Luo, K Chen, Z Tian… - IEEE Transactions on …, 2018 - ieeexplore.ieee.org
The rapid increase in the number of Android malware poses great challenges to anti-
malware systems, because the sheer number of malware samples overwhelms malware …

Automated poisoning attacks and defenses in malware detection systems: An adversarial machine learning approach

S Chen, M Xue, L Fan, S Hao, L Xu, H Zhu, B Li - computers & security, 2018 - Elsevier
The evolution of mobile malware poses a serious threat to smartphone security. Today,
sophisticated attackers can adapt by maximally sabotaging machine-learning classifiers via …

Droidevolver: Self-evolving android malware detection system

K Xu, Y Li, R Deng, K Chen, J Xu - 2019 IEEE European …, 2019 - ieeexplore.ieee.org
Given the frequent changes in the Android framework and the continuous evolution of
Android malware, it is challenging to detect malware over time in an effective and scalable …

Lightweight, obfuscation-resilient detection and family identification of android malware

J Garcia, M Hammad, S Malek - ACM Transactions on Software …, 2018 - dl.acm.org
The number of malicious Android apps is increasing rapidly. Android malware can damage
or alter other files or settings, install additional applications, and so on. To determine such …

End-to-end malware detection for android IoT devices using deep learning

Z Ren, H Wu, Q Ning, I Hussain, B Chen - Ad Hoc Networks, 2020 - Elsevier
Abstract The Internet of Things (IoT) has grown rapidly in recent years and has become one
of the most active areas in the global market. As an open source platform with a large …

Deeprefiner: Multi-layer android malware detection system applying deep neural networks

K Xu, Y Li, RH Deng, K Chen - 2018 IEEE European …, 2018 - ieeexplore.ieee.org
As malicious behaviors vary significantly across mobile malware, it is challenging to detect
malware both efficiently and effectively. Also due to the continuous evolution of malicious …

Finding unknown malice in 10 seconds: Mass vetting for new threats at the {Google-Play} scale

K Chen, P Wang, Y Lee, XF Wang, N Zhang… - 24th USENIX Security …, 2015 - usenix.org
An app market's vetting process is expected to be scalable and effective. However, today's
vetting mechanisms are slow and less capable of catching new threats. In our research, we …

Deep learning for image-based mobile malware detection

F Mercaldo, A Santone - Journal of Computer Virology and Hacking …, 2020 - Springer
Current anti-malware technologies in last years demonstrated their evident weaknesses due
to the signature-based approach adoption. Many alternative solutions were provided by the …